Sabitlenmiş Tweet
Can you spot which of these AllTrails ads are real? And which were created by @superscale_ai?
English
Ben Pflugpeil
300 posts
Ben Pflugpeil retweetledi
The best proof of Superscale's capabilities isn't us showcasing it.
It's our customer's KPIs after using Superscale.
- reduced costs
- doubled user acquisition
- and scaled ad output with >5x
Read the thread below to see how Superscale transformed their whole marketing workflows:
English
This is exactly what every company should do right now to increase ROAS:
- watch how other companies do successful marketing
- copy the exact steps
- and use a platform like @superscale_ai to automate all of it
Superscale@superscale_ai
The best proof of Superscale's capabilities isn't us showcasing it. It's our customer's KPIs after using Superscale. - reduced costs - doubled user acquisition - and scaled ad output with >5x Read the thread below to see how Superscale transformed their whole marketing workflows:
English
@_otuboi the consolidation play is smart. when design, copy, and distribution live in one tool, you cut 3 handoffs out of every campaign. we've seen teams go from weeks to hours just by removing the tool-switching tax.
English
Canva isn’t just a design tool anymore.
They just doubled down on AI + marketing automation with Simtheory & Ortto.
Next-level growth moves.
TechCrunch@TechCrunch
Canva doubles down on AI and marketing automation with Simtheory, Ortto acquisitions techcrunch.com/2026/04/08/can…
English
@Skynetjoe1 had a client spending $4k/month on a designer for social content. set up something similar, now they're shipping 5x the volume and the designer moved to brand strategy instead. the "one prompt" part sounds simple but getting that prompt right is where all the leverage is.
English
● I gave Claude Code one prompt.
It gave me back:
→ 30 carousels, 339 slides
→ 6 design styles
→ Posts to LinkedIn, IG, FB & Pinterest
→ 6 weeks of content, auto-scheduled
No Canva. No designer. One AI agent.
#ClaudeCode #AI #ContentCreation #AITools
English
@TaurusCoder the "confidently do the wrong thing" part is painfully accurate
English
Solo founder reality: I have more AI agents than employees. They don't complain. They don't have off days. They also hallucinate, miss context, and confidently do the wrong thing. Still worth it. Just not the way the demos show.
English
@growthmanx29 the "fits into a system" part is what kills most of them. same thing on the marketing side, people automate production but skip distribution entirely. fast to build, invisible to customers.
English
@IAmAaronWill do you find the Notion memory layer holds up when volume scales? that's been the bottleneck in every similar stack we've tested
English
@Fintech03 we're seeing this play out with clients already. the moment you go from 'AI helps the user' to 'AI does the work for the user' retention changes completely. people don't churn from something that's actively doing their job.
English
Never write-off anyone. Unlike traditional SaaS, which grows roughly linearly with the number of users, Agentic AI has the potential to scale revenue much faster because the AI itself performs many actions on behalf of the user.
Perplexity demonstrated this when their agents became capable of reliable multi-step workflows (research, shopping, booking, tax filing, etc.). Instead of just charging for logins/seats, they shifted toward usage-based pricing that monetizes the actual work the agent does. This agentic flywheel: better agents leading to higher usage per user has been a major driver of their recent revenue growth.
The Kobeissi Letter@KobeissiLetter
BREAKING: Perplexity's revenue has reportedly surged +50% in one month after shifting into AI agents, per FT. As a result, Perplexity's revenue has doubled in one quarter to more than $450 million in ARR. This follows Anthropic's push into the space which said its ARR hit $19 billion at the end of February. AI agents are skyrocketing in popularity.
English
@dcohendumani the workflow didn't die though, the QA process was just missing. we treat prompt auditing like code review now and it catches stuff way faster than any manual spot-check ever did.
English
Your AI workflow is working.
Nobody checked the prompts.
That is the exact pattern I see constantly.
A consulting firm saves 12 hours a week.
Then week 8 happens.
Compliance finds PII in the data.
The workflow dies immediately.
English
@shannholmberg the webinar repurposing one hit home. client was sitting on 40+ recorded sales calls doing nothing. ran them through a workflow, had 3 months of content ready in a week. the raw material was always there, the cost to extract it just wasn't worth it until now.
English
how to achieve anthropic's level 3 of AI marketing
level 3 is doing work you never attempted because the manual cost made it pointless
> go through every single search term on a high-spend Google account daily to find negative keywords. you're not doing that across tens of thousands of terms manually
> QA your entire site top to bottom every day. dead URLs, pixel misfires, busted UTMs, broken redirects. each one is small but collectively they cost you 10-20%
> monitor every competitor's content, pricing, and positioning weekly, not once a quarter
> A/B test 50 headline variations, not 2
> turn every webinar into blog posts, social clips, email sequences, and SEO articles
> run audience sentiment analysis across thousands of posts to track how messaging shifts in your space over time
I've wanted us to do things at our agency for years that I knew were negative ROI. now that threshold barely exists
we can even run small pro-bono projects for clients using our agents, things that would never have been worth the hours before but now cost a few API calls to deliver
the insights were always there, we just couldnt afford to go get them
Shann³@shannholmberg
anthropics growth marketer mapped out 4 levels of AI marketing use most people sit at level 1, automating what they already do > level 1: automate what you already do (reporting, copy, data pulls) > level 2: use AI as a thinking partner where its better than you > level 3: do work that was below the ROI threshold before > level 4: build custom tools only you would ever build level 3 is work that never existed before. stuff nobody did because the manual cost was never worth it mining negative keywords across every ad group. checking your full site for broken links daily same logic applies to content, research, QA, competitor monitoring. all work that existed in theory but nobody had the hours for level 4 is where the ROI compounds there are hundreds of AI marketing skills and plugins floating around github right now. most of them work in theory but fall apart in practice because they are built for the general case, not your case your business has specific data, specific workflows, specific edge cases that no generic tool will ever cover. the people building custom tools around their own problems are the ones pulling ahead
English
@forgebitz think the real split isn't generic vs specialized though. it's context vs no context. a general model plugged into your actual customer data and campaign history beats a 'marketing agent' that starts cold every time.
English
a lot of ai saas companies will get lost in making generic agents
"marketing agents" that aren't really good at any marketing or "finance agents" that barely can parse a csv file
right now all the problems are so extremely complex on their own that just solving one thing is the key
customers are not interested in generic agents that can't perform well; they would rather have a dedicated agent/saas who absolutly nails one thing
you only care about the best ones, if you have to pick between generic marketing agents or one that is 20% better at just running ads or writing content, what do you pick?
English
Ben Pflugpeil retweetledi
Finding a winning hook is 80% of the battle.
The other 20%? Variating on it until the algorithm can't stop serving it.
Here's the playbook:
→ Find what's already working in your niche
→ Reverse-engineer the hook
→ Generate dozens of variations: new angles, new formats, new CTAs
→ Launch across Meta, TikTok & Google simultaneously
→ Kill what doesn't work. Scale what does.
Taxfix ran this exact approach with Superscale:
+39% CTR, +37% thumbstop ratio, 200+ ads shipped.
Most teams spend weeks on one concept.
Superscale Agent lets you test hundreds in the time it takes to brief one.
Stop guessing. Start variating.
English
@kbrayatl the 'neither is sufficient alone' part is what people miss. everyone talks about AI replacing devs but the real unlock is domain experts who couldn't build before suddenly shipping production software. that changes who gets to be a founder.
English
20 years in B2B sales. Zero years as a developer. Built a production SaaS app using AI coding tools. My job was product decisions and sales expertise. Claude Code wrote the TypeScript. Both sides are necessary. Neither is sufficient alone.
English
@MichaelRouveure curious how the AI handled the booking edge cases after 9 years of accumulated business logic. that's usually where these rebuilds get messy
English
I gave 4 AI tools the same impossible job:
Rebuild a 9-year-old SaaS that processes millions in bookings.
360 tickets. Full Figma designs. Same inputs.
Only one survived.
Here's what happened 🧵
English
English
@ujjwalscript curious where you think the line is between reviewing AI output and actually understanding what it built. like at what point does the auditing become slower than just writing it yourself?
English
How to NOT get laid off in 2026 as a Dev:
1. Own the Architecture: AI is a terrible system designer. Your value is defining strict system boundaries, data contracts, and cloud infrastructure before the AI touches the codebase.
2. Stop fully "Vibe Coding": Stop letting agents blindly autocomplete your projects into a technical debt spiral. Leverage workflows like Cursor's Plan Mode to force the AI to generate static design files and structural blueprints first. Review the blueprint, then let the machine build.
3. Master the "Hallucination Hunt": The fastest way to get fired today is deploying AI-generated code that contains an invisible security flaw or a massive Big-O inefficiency. Your real job is now Code Auditing. You are the Editor-in-Chief.
English
ran into this exact thing with a SaaS client last month. they had 4 AI pilots running, all technically working. but nothing shipped because every output still needed to go through a 5-person review chain that existed before AI. killed the review chain, kept one person with judgment. suddenly the pilots started producing real results.
English
AI feels like it’s everywhere now. Spend a few days in San Francisco and it feels like the future has already arrived – agents, autonomy, AI-native companies. It creates a powerful illusion: the rest of the world is moving at the same speed. But it isn’t.
Most companies are still at a much earlier stage. For many, AI means ChatGPT for writing, Copilot for code, maybe a few internal experiments, and a lot of pressure to “do something with AI.”
The real gap is the organization itself.
Work inside companies is still largely invisible to machines. Processes live in people’s heads, data is fragmented, and decisions don’t follow the org chart.
That’s why so many AI pilots look impressive, and then quietly disappear. Because AI adoption isn’t a straight line, it's a stack of dependencies.
You can’t jump to agents if workflows aren’t legible.
You can’t act on data you don’t trust.
You can’t automate decisions that aren’t clearly defined.
The real work sits in the middle:
• Making the organization legible to itself
• Making data trustworthy and verifiable
• Letting systems act and reshaping roles around that
• Closing the loop so systems learn from human decisions
That’s where deployments either become real or die. And it’s not really about AI – it's more about organizational redesign. AI adds intelligence while forcing companies to confront how they actually work.
And the winners will be those who did the unsexy work of becoming organizations AI can actually understand and operate within.
We break this down with @wschenk in our article → The Unsexy Truth of AI Adoption turingpost.com/p/orgage2
And I'm really interested in how this looks inside your companies. Where are you getting stuck?
English
@Adityarou6688 the framing of one agent per function is interesting but in practice the biggest wins come from chaining them. lead follow-up agent feeds context into onboarding, which feeds reporting. stops being 3 separate tools and starts being one system with memory.
English
@RebienGhazali the real unlock is that 8-dimension scoring happening in real-time, not after the call. most sales tools give you a post-mortem. by the time you read the report the deal is already won or lost.
English
Every sales team will have an AI co-pilot by 2027.
The question isn't if.
The question is: are you the one building it, or the one getting replaced by it?
I chose to build it. 6 months, one apartment in Dubai, and a lot of Claude API calls later — Salesflux scores every sales call on 8 dimensions in real-time.
Discovery depth. Objection handling. Emotional connection. Authenticity. All of it.
The closers who use it improved their close rate by 40% in the first month.
Not because the AI closes for them. Because it shows them exactly what they're doing wrong — while they can still fix it.
English
same pattern with marketing automation. client demos come together in a day, everyone's impressed, then the actual workflows break the moment real campaign data hits them. had to split our process the same way – fast for prototypes, structured for production. that 80/20 boundary is real.
English
vibe coding gets you to 80% in days. then it stalls.
for talehop.com I treated the two halves differently: lovable for the frontend prototype, claude code with a strict plan-first workflow for the FastAPI backend. the second half is what kept us from becoming another half-done app.
#IndieHacker #SoloDev
English